People

Dr Spyros Samothrakis

Lecturer (R) and Deputy Director of IADS
School of Computer Science and Electronic Engineering (CSEE)
Dr Spyros Samothrakis
  • Email

  • Telephone

    +44 (0) 1206 872683

  • Location

    parkside block c2, Colchester Campus

Profile

Qualifications

  • 2014, PhD Computer Science,University of Essex

  • 2007, MSc Intelligent Systems, University of Sussex

  • 2003, BSc Computer Science, University of Sheffield

Research and professional activities

Research interests

Reinforcement Learning

Open to supervise

Machine Learning

Open to supervise

Neural Networks

Open to supervise

Role Playing Games

Open to supervise

Teaching and supervision

Current teaching responsibilities

  • Data Science and Decision Making (CE888)

Previous supervision

Umar Isyaku Abdullahi
Umar Isyaku Abdullahi
Degree subject: Advanced Computer Science
Degree type: Master of Science
Awarded date: 5/10/2016

Publications

Journal articles (15)

Samothrakis, S., (2018). Viewpoint: Artificial intelligence and labour. IJCAI International Joint Conference on Artificial Intelligence. 2018-July, 5652-5655

Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents: Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8. Genetic Programming and Evolvable Machines. 19 (4), 567-568

Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents - Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8.. Genetic Programming and Evolvable Machines. 19, 567-568

Tom Vodopivec, Samothrakis, S. and Brank Ster, (2017). On monte carlo tree search and reinforcement learning. The Journal of Artificial Intelligence Research. 60, 881-936

Samothrakis, S., Fasli, M., Perez, D. and Lucas, S., (2017). Default policies for global optimisation of noisy functions with severe noise. Journal of Global Optimization. 67 (4), 893-907

Samothrakis, S., Perez, D., Lucas, SM. and Rohlfshagen, P., (2016). Predicting Dominance Rankings for Score-Based Games. IEEE Transactions on Computational Intelligence and AI in Games. 8 (1), 1-12

Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, SM., Couetoux, A., Lee, J., Lim, C-U. and Thompson, T., (2016). The 2014 General Video Game Playing Competition. IEEE Transactions on Computational Intelligence and AI in Games. 8 (3), 229-243

Perez, D., Mostaghim, S., Samothrakis, S. and Lucas, SM., (2015). Multiobjective Monte Carlo Tree Search for Real-Time Games. IEEE Transactions on Computational Intelligence and AI in Games. 7 (4), 347-360

Samothrakis, S. and Fasli, M., (2015). Emotional Sentence Annotation Helps Predict Fiction Genre. PLOS ONE. 10 (11), e0141922-e0141922

Perez, D., Powley, EJ., Whitehouse, D., Rohlfshagen, P., Samothrakis, S., Cowling, PI. and Lucas, SM., (2014). Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions. IEEE Transactions on Computational Intelligence and AI in Games. 6 (1), 31-45

Perez, D., Togelius, J., Samothrakis, S., Rohlfshagen, P. and Lucas, SM., (2014). Automated Map Generation for the Physical Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation. 18 (5), 708-720

Samothrakis, S., Lucas, S., Runarsson, T. and Robles, D., (2013). Coevolving Game-Playing Agents: Measuring Performance and Intransitivities. IEEE Transactions on Evolutionary Computation. 17 (2), 213-226

Friston, K., Samothrakis, S. and Montague, R., (2012). Active inference and agency: optimal control without cost functions. Biological Cybernetics. 106 (8-9), 523-541

Browne, CB., Powley, E., Whitehouse, D., Lucas, SM., Cowling, PI., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. and Colton, S., (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games. 4 (1), 1-43

Samothrakis, S., Robles, D. and Lucas, S., (2011). Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man. IEEE Transactions on Computational Intelligence and AI in Games. 3 (2), 142-154

Conferences (20)

Samothrakis, S., (2018). Viewpoint: Artificial Intelligence and Labour.

Alshahrani, M., Samothrakis, S. and Fasli, M., (2017). Word mover's distance for affect detection

Abdullahi, U., Samothrakis, S. and Fasli, M., (2017). Counterfactual domain adversarial training of neural networks

Alshahrani, M., Samothrakis, S., Fasli, M. and IEEE, (2017). Word Mover's Distance for Affect Detection

Abdullahi, UI., Samothrakis, S., Fasli, M. and IEEE, (2017). Counterfactual Domain Adversarial Training of Neural Networks

Samothrakis, S., Vodopivec, T., Fairbank, M. and Fasli, M., (2017). Convolutional-Match Networks for Question Answering

Perez-Liebana, D., Samothrakis, S., Togelius, J., Lucas, SM. and Schaul, T., (2016). General video game AI: Competition, challenges, and opportunities

Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T. and Lucas, SM., (2016). Analyzing the robustness of general video game playing agents

Samothrakis, S., Vodopivec, T., Fasli, M. and Fairbank, M., (2016). Match memory recurrent networks

Samothrakis, S., Perez-Liebana, D., Lucas, SM. and Fasli, M., (2015). Neuroevolution for General Video Game Playing

Perez, D., Powley, E., Whitehouse, D., Samothrakis, S., Lucas, S. and Cowling, PI., (2014). The 2013 Multi-objective Physical Travelling Salesman Problem Competition

Perez, D., Samothrakis, S. and Lucas, S., (2014). Knowledge-based fast evolutionary MCTS for general video game playing

Samothrakis, S., Roberts, SA., Perez, D. and Lucas, SM., (2014). Rolling horizon methods for games with continuous states and actions

Lucas, SM., Samothrakis, S. and PĂ©rez, D., (2014). Fast Evolutionary Adaptation for Monte Carlo Tree Search

Perez, D., Samothrakis, S. and Lucas, S., (2013). Online and offline learning in multi-objective Monte Carlo Tree Search

Perez, D., Samothrakis, S., Lucas, S. and Rohlfshagen, P., (2013). Rolling horizon evolution versus tree search for navigation in single-player real-time games

Ashlock, D., Ashlock, W., Samothrakis, S., Lucas, S. and Lee, C., (2012). From competition to cooperation: Co-evolution in a rewards continuum

Samothrakis, S. and Lucas, S., (2011). Approximating n-player behavioural strategy nash equilibria using coevolution

Samothrakis, S., Rob, D. and Lucas, SM., (2010). A UCT agent for Tron: Initial investigations

Samothrakis, S. and Lucas, SM., (2010). Planning using online evolutionary overfitting

Grants and funding

2018

Discovering Individual and Social Preferences through Inverse Reinforcement Learning

Economic and Social Research Council

2017

Embedding a Machine Learning capability into the Hood Group Ltd platform.

Innovate UK (formerly Technology Stategy Board)

Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.

Prequin

To embed a NLP capability in Objective IT

Innovate UK (formerly Technology Stategy Board)

The project investigates the use of algorithms (genetic + reinforcement) to provide accurate forecasts of asset prices.

Innovate UK (formerly Technology Stategy Board)

Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.

Prequin

2016

67% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms

Technology STrategy Board

33% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms

Mondaq Ltd

67% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.

Technology STrategy Board

33% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.

Orbital Media & Advertising Ltd.

Scoping Exercise for new data product

Hood Group Ltd

2015

67% - To extend the business intelligence and digital marketing offer by developing and embedding a new data analytics capability

Technology STrategy Board

33% - To extend the business intelligence and digitial marketing offer by developing and embedding a new data analytics capability

Objective Computing Ltd

Contact

ssamot@essex.ac.uk
+44 (0) 1206 872683

Location:

parkside block c2, Colchester Campus